Lower limb loading assessment systems and methods

ABSTRACT

A lower limb loading assessment system having at least one motion sensor mounted to a subject&#39;s lower limb that is configured to sense the tibial shockwaves experienced by the lower limb as the subject performs a repetitive physical activity involving repetitive footstrikes of the lower limb with a surface. The motion sensor comprises an accelerometer that is configured to sense acceleration data in at least three axes and generate representative acceleration data over a time period associated with the physical activity. The acceleration data represents a series of discrete tibial shockwaves from the discrete footstrikes. A data processor receives the tibial shockwave data and processes that to generate output feedback data comprising data to assist the subject to minimize future loading in their lower limbs.

PRIORITY CLAIM TO RELATED APPLICATIONS

This application is a continuation and claims the benefit of priority ofU.S. application Ser. No. 15/128,808, filed 23 Sep. 2016, which is aU.S. national stage application filed under 35 U.S.C. § 371 fromInternational Application Serial No. PCT/IB2015/051206, which was filed18 Feb. 2015, and published as WO2015/145273 on 1 Oct. 2015, and whichclaims priority to New Zealand Application No. 622954, filed 25 Mar.2014, which applications and publication are incorporated by referenceas if reproduced herein and made a part hereof in their entirety, andthe benefit of priority of each of which is claimed herein.

FIELD OF THE INVENTION

The present invention relates to lower limb loading assessment systemsand methods for activities such as, but not limited to, running.

BACKGROUND TO THE INVENTION

Musculoskeletal tissues, such as bone, muscle, tendon and cartilage,respond and adapt to their local mechanical environment in such a manneras to maintain a stable equilibrium, or homeostasis. Mechanical loadsare also responsible for injury to musculoskeletal tissue and arecritical for the rehabilitation and regeneration of the tissue. In itsbroadest sense, injury occurs when the loads experienced by the tissueexceed the strength of that tissue. These loads might be traumatic, suchas a direct impact or single loading event causing failure, orcumulative, where repeated loads result in damage.

During running, for example, reaction forces of 2-3 times body weightare transmitted from the ground, through the foot and into the lowerlimb via the ankle, knee and hip joints. The musculoskeletal tissues,particularly muscle and tendon, attenuate transient impact loads as theytravel up the limb. Over the course of a 5 km run, the average runnerwill strike the ground approximately 3,000 times and this repetitiveloading has been associated with common overuse injuries, such aspatellofemoral pain, plantar fasciitis, fatigue fractures, and Achillestendinopathy.

In this specification where reference has been made to patentspecifications, other external documents, or other sources ofinformation, this is generally for the purpose of providing a contextfor discussing the features of the invention. Unless specifically statedotherwise, reference to such external documents is not to be construedas an admission that such documents, or such sources of information, inany jurisdiction, are prior art, or form part of the common generalknowledge in the art.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide lower limb loadingassessment systems and methods which generate outputs that can be usedto minimize lower limb injury, or to at least provide the public with auseful choice.

In a first aspect, the invention broadly consists in a lower limbloading assessment system comprising: at least one motion sensor mountedto a subject's lower limb that is configured to sense the tibialshockwaves experienced by the lower limb as the subject performs arepetitive physical activity involving repetitive footstrikes of thelower limb with a surface, the motion sensor comprising a anaccelerometer that is configured to sense acceleration data in at leastthree axes and generate representative acceleration data over a timeperiod associated with the physical activity, the motion sensorgenerating tibial shockwave data comprising the generated accelerationdata which represents a series of discrete tibial shockwaves from thediscrete footstrikes; and a data processor that is configured to receivethe tibial shockwave data sensed by the motion sensor, and wherein thedata processor is configured to process the received tibial shockwavedata to generate output feedback data comprising data to assist thesubject to minimize future loading in their lower limbs.

In an embodiment, the data processor is configured to extract orcalculate one or more variables from the received tibial shockwave dataand compare the or each variable to a predetermined threshold orthresholds and provide feedback data in the form of a real-time alertsignal if one or more of the thresholds is exceeded by its associatedvariable.

In an embodiment, the data processor is configured to: convert the3-axes acceleration data of the tibial shockwave data into resultantacceleration magnitude data and extract peak shock variablesrepresenting the peak resultant acceleration magnitude data associatedwith each discrete footstrike.

In an embodiment, the data processor is configured to generate areal-time alert signal if any peak shock variables exceed apredetermined threshold.

In an embodiment, the data processor is configured to calculate anaverage peak shock variable representing the average of the extractedpeak shock variables, and wherein the data processor is configured togenerate a real-time alert signal if the average peak shock variableexceeds a predetermined threshold.

In an embodiment, the data processor is configured to generatefootstrike pattern variables representing the footstrike patternassociated with each footstrike as defined by the profile of theresultant acceleration magnitude data for a period associated with eachdiscrete footstrike and generate a real-time alert signal if any of thefootstrike pattern variables exceed a predetermined footstrike patternthreshold.

In an embodiment, the data processor is configured to generatefootstrike pattern variables representing the footstrike patternassociated with each footstrike as defined by the profile of theacceleration data in three axes for a period associated with eachdiscrete footstrike and generate a real-time alert signal if any of thefootstrike pattern variables exceed a predetermined footstrike patternthreshold.

In an embodiment, the data processor is configured to generate thefootstrike pattern variables based on tibial shockwave data for eachdiscrete footstrike between heelstrike and toe-off time locations.

In an embodiment, the system further comprises one or more feedbackdevices mounted to or carried by the user that are triggered by inresponse to a generated real-time alert signal.

In an embodiment, the feedback devices comprise any one or more of thefollowing: tactile feedback devices, audible feedback devices, and/orvisual feedback devices.

In an embodiment, the data processor is configured to process the tibialshockwave data to generate feedback data in the form of data indicativeof a session load stimulus.

In an embodiment, the data processor is configured to receive tibialshockwave data from a plurality of activity sessions of the subject froma single day and generate feedback data in the form data indicative of adaily load stimulus.

In an embodiment, the data processor is configured to identify the timelocations of the heelstrikes associated with each footstrike andgenerate feedback data in the form of cadence representing the averagetime between heelstrikes.

In an embodiment, the data processor is configured to: receive tibialshockwave data from a plurality of separate activity sessions, convertthe 3-axes acceleration data of the tibial shockwave data into resultantacceleration magnitude data, extract peak shock values representing thepeak resultant acceleration magnitude associated with each discretefootstrike of the tibial shockwave data of each of the separate activitysessions, calculate the average peak resultant acceleration magnitudefor each of the separate activity sessions based on the extracted peakshock values, and generate feedback data representing the calculatedaverage peak resultant acceleration magnitude for each separate activitysession.

In an embodiment, the subject is wearing a different type of footwear ineach separate activity session, and the data processor is configured toreceive or associate unique identification data relating to eachdifferent type of footwear used by the subject with the respectivetibial shockwave data of each activity session, and the feedback datagenerated comprises data representing the calculated average peakresultant acceleration magnitude of each separate activity sessionlinked with the unique identification data relating to the footwear usedin the activity session.

In an embodiment, the data processor is further configured to comparethe calculated average peak resultant acceleration magnitude associatedwith each activity session and generate further feedback dataidentifying the activity session having the lowest peak resultantacceleration magnitude.

In an embodiment, the three axes of the 3-axis accelerometer areorthogonal to each other.

In an embodiment, the data processor is communicatively coupled to themotion sensor over a data link. In another embodiment, the precedingclaims wherein the data processor is onboard the motion sensor.

In an embodiment, the motion sensor is mounted to the subject's lowerlimb between the femoral epicondyle and medial malleolus.

In an embodiment, the motion sensor is mounted to the subject's lowerlimb in the region of the lower ⅓^(rd) of the tibia.

In an embodiment, the motion sensor is mounted to the subject's lowerlimb in the region of the medial part of the tibia.

In an embodiment, the motion sensor is mounted to the subject's lowerlimb in the region adjacent and above the medial malleolus of the tibia.

In an embodiment, the motion sensor is mounted to the subject's lowerlimb in the region adjacent and above the lateral malleolus of thetibia.

In a second aspect, the invention broadly consists in a method ofassessing the loading on a subject's lower limb at the subject performsa repetitive physical activity involving repetitive footstrikes of thelower limb with a surface, the method implemented on a computing deviceand comprising: receiving tibial shockwave data comprising sensedacceleration data from at least one motion sensor mounted to thesubject's lower limb that comprises an accelerometer that is configuredto sense and generate acceleration data in at least three axes, thesensed acceleration data representing a series of discrete tibialshockwaves from the discrete footstrikes; and processing the tibialshockwave data to generate output feedback data comprising data toassist the subject to minimize future loading in their lower limbs.

In an embodiment, the method comprises extracting or calculating one ormore variables from the received tibial shockwave data, comparing the oreach variable to a predetermined threshold or thresholds, and generatingfeedback data in the form of a real-time alert signal if one or more ofthe thresholds is exceeded by its associated variable.

In an embodiment, the method comprises converting the 3-axesacceleration data of the tibial shockwave data into resultantacceleration magnitude data and extracting peak shock variablesrepresenting the peak resultant acceleration magnitude data associatedwith each discrete footstrike.

In an embodiment, the method further comprises generating a real-timealert signal if any peak shock variables exceed a predeterminedthreshold.

In an embodiment, the method further comprises calculating an averagepeak shock variable representing the average of the extracted peak shockvariables and generating a real-time alert signal if the average peakshock variable exceeds a predetermined threshold.

In an embodiment, the method further comprises generating footstrikepattern variables representing the footstrike pattern associated witheach footstrike as defined by the profile of the resultant accelerationmagnitude data for a period associated with each discrete footstrike andgenerating a real-time alert signal if any of the footstrike patternvariables exceed a predetermined footstrike pattern threshold.

In an embodiment, the method further comprises generating footstrikepattern variables representing the footstrike pattern associated witheach footstrike as defined by the profile of the acceleration data inthree axes for a period associated with each discrete footstrike andgenerating a real-time alert signal if any of the footstrike patternvariables exceed a predetermined footstrike pattern threshold.

In an embodiment, the method comprises generating the footstrike patternvariables by based on tibial shockwave data for each discrete footstrikebetween heelstrike and toe-off time locations.

In an embodiment, the method comprises triggering one or more feedbackdevices mounted to or carried by the user in response to a generatedreal-time alert signal.

In an embodiment, the feedback devices comprise any one or more of thefollowing: tactile feedback devices, audible feedback devices, and/orvisual feedback devices.

In an embodiment, the method comprises processing the tibial shockwavedata to generate feedback data in the form of data indicative of asession load stimulus.

In an embodiment, the method comprises receiving tibial shockwave datafrom a plurality of activity sessions of the subject from a single dayand generating feedback data in the form data indicative of a daily loadstimulus.

In an embodiment, the method comprises identifying the time locations ofthe heelstrikes associated with each footstrike and generating feedbackdata in the form of cadence representing the average time betweenheelstrikes.

In an embodiment, the method comprises receiving the tibial shockwavedata from a plurality of separate activity sessions, converting the3-axes acceleration data of the tibial shockwave data into resultantacceleration magnitude data, extracting peak shock values representingthe peak resultant acceleration magnitude associated with each discretefootstrike of the tibial shockwave data of each of the separate activitysessions, calculating the average peak resultant acceleration magnitudefor each of the separate activity sessions based on the extracted peakshock values, and generating feedback data representing the calculatedaverage peak resultant acceleration magnitude for each separate activitysession.

In an embodiment, the subject is wearing a different type of footwear ineach separate activity session, and the data processor is configured toreceive or associate unique identification data relating to eachdifferent type of footwear used by the subject with the respectivetibial shockwave data of each activity session, and the feedback datagenerated comprises data representing the calculated average peakresultant acceleration magnitude of each separate activity sessionlinked with the unique identification data relating to the footwear usedin the activity session.

In an embodiment, the method comprises comparing the calculated averagepeak resultant acceleration magnitude associated with each activitysession and generating further feedback data identifying the activitysession having the lowest peak resultant acceleration magnitude.

In an embodiment, the three axes of the 3-axis accelerometer areorthogonal to each other.

In an embodiment, the motion sensor is mounted to the subject's lowerlimb between the femoral epicondyle and medial malleolus.

In an embodiment, the motion sensor is mounted to the subject's lowerlimb in the region of the lower ⅓^(rd) of the tibia.

In an embodiment, the motion sensor is mounted to the subject's lowerlimb in the region of the medial part of the tibia.

In an embodiment, the method comprises the motion sensor is mounted tothe subject's lower limb in the region adjacent and above the medialmalleolus of the tibia.

In an embodiment, the method comprises the motion sensor is mounted tothe subject's lower limb in the region adjacent and above the lateralmalleolus of the tibia.

In a third aspect, the invention broadly consists in a lower limbloading assessment system comprising:

-   -   at least one motion sensor mounted to a subject's lower limb        that is configured to sense the tibial shockwaves experienced by        the lower limb as the subject performs a repetitive physical        activity involving repetitive footstrikes of the lower limb with        a surface, the motion sensor generating tibial shockwave data        representing a series of discrete tibial shockwaves from the        discrete footstrikes; and    -   a data processor that is configured to receive the tibial        shockwave data sensed by the motion sensor, and wherein the data        processor is configured to process the received tibial shockwave        data to generate output feedback data comprising data to assist        the subject to minimize future loading in their lower limbs.

In an embodiment, the motion sensor comprises an accelerometer that isconfigured to sense acceleration data in at least three axes andgenerate representative acceleration data over a time period associatedwith the physical activity, the tibial shockwave data comprising theacceleration data.

In a fourth aspect the invention broadly consists in a computer-readablemedium having stored thereon computer executable instructions that, whenexecuted on a processing device, cause the processing device to performthe method of the second aspect of the invention.

The third and fourth aspects of the invention may have any one or moreof the features mentioned in respect of the first and second aspects ofthe invention.

Other configurations are also described below.

Also described is a first configuration comprising a lower limb shockassessment system comprising: one or more motion sensors mounted to asubject's lower limb which are configured to sense the tibial shockwavesexperienced by the lower limb as the subject performs a repetitivephysical activity and which generate representative tibial shockwavedata; and a computing device that is configured to receive the tibialshockwave data sensed by the one or more sensors from a plurality ofseparate activity sessions, the subject performing the same repetitivephysical activity in each activity session, and wherein the processor isconfigured to generate assessment data based on the tibial shockwavedata from the activity sessions.

In one embodiment, the sensor(s) are configured to transmit the tibialshockwave data to the computing device over a wireless communicationmedium. In another embodiment, the sensor(s) are configured to transmitthe tibial shockwave data to the computing device over a hardwiredcommunication medium.

In one form, the sensor(s) may comprise a transmitter module fortransmitting the data to the computing device either directly, or via anintermediate receiver module operatively connected to the computingdevice.

In an embodiment, the system comprises a single motion sensor mounted tothe subject's lower limb. The motion sensor may comprise a 3-axisaccelerometer. The 3-axis accelerometer may be configured to measure rawacceleration data with respect to three separate axes. In one form, thethree axes are orthogonal to each other. In this form, the rawthree-axes acceleration data corresponds to the tibial shockwave data.

In one form, the motion sensor is configured to generate resultantacceleration magnitude data based on the raw three-axis accelerationdata, and this resultant acceleration magnitude data represents thetibial shockwave data. In another form, the computing device receivesthe raw three-axis acceleration data from the motion sensor andgenerates the resultant acceleration magnitude data representing thetibial shockwave data.

In one embodiment, the repetitive physical activity is running orwalking on a surface. In this embodiment, the tibial shockwave datacomprises data representing a series of discrete tibial shockwaves, eachtibial shockwave corresponding to a discrete foot strike when thesubject's foot strikes the surface. In one form, the tibial shockwavedata for each activity session is sensed for a predetermined timeperiod. Typically, the predetermined time period is identical for eachactivity session.

In one embodiment, the computing device is configured to: determine thepeak resultant acceleration magnitude for each discrete tibial shockwavein the series of the activity session; and calculate the average peakresultant acceleration magnitude over the series. In one form, thecomputing device is configured to generate a tibial shock score for eachactivity session based on or corresponding to the determined averagepeak resultant acceleration magnitude of the series of discrete tibialshockwaves in the activity session. In one embodiment, the tibial shockscore may be the average peak resultant acceleration magnitude or themagnitude converted into a normalized value within a predeterminedtibial shock score scale.

In one example, the subject may be wearing a different type (e.g.,style, model, size) of footwear for each activity session. In oneconfiguration, the computing device may be configured to receive orassociate unique identification data relating to each different type offootwear used by the subject with the respective tibial shockwave dataof each activity session and is further configured to generateassessment data associating the tibial shock score of each activitysession with the footwear used in the activity session. In anotherconfiguration, the computing device may be configured to generateassessment data representing the tibial shock score for each activitysession.

In one configuration, the computing device may be configured to generateassessment data based on a comparison of the tibial shock scores fromeach activity session. In one example, the computing device may beconfigured to generate assessment data which identifies the activitysession having the lowest tibial shock score, which corresponds to thelowest overall tibial shock experienced by the subject's lower limbduring the activity session. In a further example, if each activitysession is linked to a unique identification data relating to thefootwear used in the activity session, the computing device may beconfigured to output assessment data relating to the footwear having thelowest tibial shock score for the subject.

In one form, the computing device comprises a display for displaying thetibial shockwave data and/or assessment data. The data may be displayednumerically, table-form, graphically, or a combination of these.

In one form, the subject may be a human and the assessment system may beemployed for assessing and comparing the tibial shock experienced by thehuman when running in different types of footwear.

Also described is a second configuration comprising a method ofassessing lower limb shock of a subject over a plurality of activitysessions, comprising: receiving tibial shockwave data from one or moremotion sensors mounted to the subject's lower limb which are configuredto sense the tibial shockwaves experienced by the lower limb as thesubject performs a repetitive physical activity and which generaterepresentative tibial shockwave data; processing the received tibialshockwave data from a plurality of separate activity sessions, thesubject performing the same repetitive physical activity in eachactivity session; and generating assessment data based on the tibialshockwave data from the activity sessions.

In one embodiment, the method comprises receiving the tibial shockwavedata from the motion sensor(s) over a wireless communication medium. Inanother embodiment, the method comprises receiving the tibial shockwavedata from the motion sensor(s) over a hardwired communication medium.

In one embodiment, the method comprises receiving the tibial shockwavedata from a transmitter module(s) of the motion sensor(s), eitherdirectly or via an intermediate receiver module in data communicationwith the transmitter module(s).

In an embodiment, the method comprises receiving the tibial shockwavedata from a single motion sensor mounted to the subject's lower limb.The motion sensor may comprise a 3-axis accelerometer. The 3-axisaccelerometer may be configured to measure raw acceleration data withrespect to three separate axes. In one form, the three axes areorthogonal to each other. In this form, the raw three-axes accelerationdata corresponds to the tibial shockwave data.

In one form, the motion sensor is configured to generate resultantacceleration magnitude data based on the raw three-axis accelerationdata, and the method comprises receiving this resultant accelerationmagnitude data representing the tibial shockwave data from the motionsensor. In another form, the method comprises receiving the rawthree-axis acceleration data from the motion sensor and calculating theresultant acceleration magnitude data representing the tibial shockwavedata.

In one embodiment, the repetitive physical activity is running orwalking on a surface. In this embodiment, the tibial shockwave datacomprises data representing a series of discrete tibial shockwaves forthe activity session, each tibial shockwave corresponding to a discretefoot strike when the subject's foot strikes the surface. In one form,the method comprises receiving tibial shockwave data sensed over apredetermined time period for each activity session. Typically, thepredetermined time period is identical for each activity session.

In one embodiment, the method further comprises determining the peakresultant acceleration magnitude for each discrete tibial shockwave inthe series of the activity session; and calculating the average peakresultant acceleration magnitude over the series. In one form, themethod further comprises generating a tibial shock score for eachactivity session based on or corresponding to the determined averagepeak resultant acceleration magnitude of the series of discrete tibialshockwaves in the activity session. In one embodiment, the tibial shockscore may be the average peak resultant acceleration magnitude or themagnitude converted into a normalized value within a predeterminedtibial shock score scale.

In one example, the subject may be wearing a different type (e.g.,style, model, size) of footwear for each activity session. In oneembodiment, the method may further comprise: receiving or associatingunique identification data relating to each different type of footwearused by the subject with the respective tibial shockwave data of eachactivity session; and generating assessment data associating the tibialshock score of each activity session with the footwear used in theactivity session. In another embodiment, the method may further comprisegenerating assessment data representing the tibial shock score for eachactivity session.

In one embodiment, the method may further comprise generating assessmentdata based on a comparison of the tibial shock scores from each activitysession. In one example, the method may comprise generating assessmentdata which identifies the activity session having the lowest tibialshock score, which corresponds to the lowest overall tibial shockexperienced by the subject's lower limb during the activity session. Ina further example, if each activity session is linked to a uniqueidentification data relating to the footwear used in the activitysession, the method may comprise generating assessment data relating tothe footwear having the lowest tibial shock score for the subject.

In one form, the method may further comprise displaying the tibialshockwave data and/or assessment data on a display screen. The data maybe displayed numerically, table-form, graphically, or a combination ofthese.

In one form, the subject may be a human and the assessment system may beemployed for assessing and comparing the tibial shock experienced by thehuman when running in different types of footwear.

Also described is a third configuration comprising a lower limb shockassessment system comprising: one or more motion sensors mounted to asubject's lower limb which are configured to sense the tibial shockwavesexperienced by the lower limb as the subject performs physical activityand which generate representative tibial shockwave data; and a computingdevice that is configured to receive the tibial shockwave data sensed bythe one or more sensors, and wherein the processor is configured toprocess the received data and generate an estimate of the subject'sdaily load stimulus (DLS).

In one embodiment, the computing device is configured to compare thegenerated DLS to a threshold DLS stored for the subject and generate analert or notification if the threshold is exceeded.

Also described is a fourth configuration comprising a method ofassessing lower limb shock of a subject, comprising: receiving tibialshockwave data from one or more motion sensors mounted to the subject'slower limb which are configured to sense the tibial shockwavesexperienced by the lower limb as the subject performs a physicalactivity and which generate representative tibial shockwave data;processing the received tibial shockwave data; and generating anestimate of the subject's daily load stimulus (DLS).

In one embodiment, the method further comprises comparing the generatedDLS to a threshold DLS stored for the subject and generating an alert ornotification if the threshold is exceeded.

The third and fourth configurations may have any one or more of thefeatures mentioned in respect of the first and second configurations ofthe invention.

Also described is a fifth configuration comprising a lower limb shockassessment system comprising: one or more motion sensors mounted to asubject's lower limb which are configured to sense the tibial shockwavesexperienced by the lower limb as the subject performs physical activityand which generate representative tibial shockwave data; and a computingdevice that is configured to receive the tibial shockwave data sensed bythe one or more sensors, and wherein the processor is configured toprocess the received data to analyse the subject's gait, and generateoutput data indicative of modifications to the subject's gait that willreduce tibial shock.

Also described is a sixth configuration comprising a method of assessinglower limb shock of a subject, comprising: receiving tibial shockwavedata from one or more motion sensors mounted to the subject's lower limbwhich are configured to sense the tibial shockwaves experienced by thelower limb as the subject performs a physical activity and whichgenerate representative tibial shockwave data; processing the receivedtibial shockwave data to analyse the subject's gait; and generatingoutput data indicative of medications to the subject's gait that willreduce tibial shock.

The fifth and sixth configurations may have any one or more of thefeatures mentioned in respect of the first-fourth configurations.

Also described is a seventh configuration comprising a computer-readablemedium having stored thereon computer executable instructions that, whenexecuted on a processing device, cause the processing device to performany of the methods or associated features of the second, fourth, andsixth configurations.

The term “comprising” as used in this specification and claims means“consisting at least in part of”. When interpreting each statement inthis specification and claims that includes the term “comprising”,features other than that or those prefaced by the term may also bepresent. Related terms such as “comprise” and “comprises” are to beinterpreted in the same manner

Number Ranges

It is intended that reference to a range of numbers disclosed herein(for example, 1 to 10) also incorporates reference to all rationalnumbers within that range (for example, 1, 1.1, 2, 3, 3.9, 4, 5, 6, 6.5,7, 8, 9 and 10) and also any range of rational numbers within that range(for example, 2 to 8, 1.5 to 5.5 and 3.1 to 4.7) and, therefore, allsub-ranges of all ranges expressly disclosed herein are hereby expresslydisclosed. These are only examples of what is specifically intended andall possible combinations of numerical values between the lowest valueand the highest value enumerated are to be considered to be expresslystated in this application in a similar manner

As used herein the term “and/or” means “and” or “or”, or both.

As used herein “(s)” following a noun means the plural and/or singularforms of the noun.

The invention consists in the foregoing and also envisages constructionsof which the following gives examples only.

BRIEF DESCRIPTION OF THE DRAWINGS

Preferred embodiments of the invention will be described by way ofexample only and with reference to the drawings, in which:

FIG. 1 is a schematic diagram of the hardware components of the lowerlimb loading assessment system in accordance with an embodiment of theinvention;

FIGS. 2A and 2B are front and side elevation view of a user wearing amotion sensor of the lower limb loading assessment system on their lowerleg;

FIG. 3 is a schematic diagram showing the sensor axes of the motionsensor with respect to the user's lower leg;

FIG. 4A is a graph depicting measured raw 3-axis acceleration dataplotted against time sensed by the motion sensor of the lower limbloading assessment system for a series of foot-strikes recorded whilethe user was running;

FIG. 4B is a graph depicting the measured raw 3-axis acceleration dataagainst time for 3 separate sensor axes as sensed by the motion sensorfor the single discrete foot-strike indicated at AA in FIG. 4A;

FIG. 5 shows graphs plotting an overlay of normalized resultantacceleration magnitude data against time, the data representing thediscrete tibial shockwaves sensed by the motion sensor over 5 separateactivity sessions, and showing how a user's footstrike pattern may varydepending on various factors;

FIGS. 6A and 6B show box-and-whisker plots and graphs plotting anoverlay of resultant acceleration magnitude data against time, the datarepresenting the discrete tibial shockwaves sensed by the motion sensorover 5 separate activity sessions in which the user's footwear isdifferent in each session;

FIGS. 7A-7D show graphs plotting resultant acceleration magnitude data,normalized with respect to body weight, against time, the datarepresenting the series of tibial shockwaves sensed by the motion sensoras the user runs on 4 different terrains, specifically road, grass, hardsand, and soft sand;

FIG. 7E shows box-and-whisker plots of the peak resultant accelerationmagnitude data, normalized with respect to body weight, of FIGS. 7A-7Dfor the 4 different terrains;

FIG. 7F shows graphs plotting an overlay of resultant accelerationmagnitude data, normalized with respect to body weight, against time,the data corresponding to that from FIGS. 7A-7D for the 4 differentterrains;

FIG. 8 shows a flow diagram of an example algorithm for data processingof received tibial shockwave data in accordance with an embodiment ofthe invention;

FIG. 9 shows a flow diagram of a real-time running gait feedback systemusing data sensed by the motion sensor of the lower limb loadingassessment system;

FIGS. 10A-10C show schematic diagrams of the hardware components of thereal-time running gait feedback system in accordance with variousconfigurations of the invention;

FIG. 11 is a flow diagram of a cumulative loading monitoring system inaccordance with an embodiment of the invention;

FIGS. 12A-12C show graphs plotting an overlay of normalized resultantacceleration magnitude data, representing the discrete tibial shockwavessensed over 4 separate activity sessions for each of three differentrunners, the runners wearing different footwear in each activitysession, the data gathered for a shoe-fitting feedback system inaccordance with an embodiment of the invention;

FIGS. 13A-13C show respective box-and-whisker plots of the normalizedpeak resultant acceleration magnitude data shown in FIGS. 11A-11C;

FIG. 14 is a flow diagram showing a follow-up assessment process afteran initial shoe-fitting process carried in accordance with theshoe-fitting feedback system; and

FIG. 15 is a flow diagram showing a long-term feedback monitoringprocess associated with the shoe-fitting feedback system.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

In the following description, specific details are given to provide athorough understanding of the embodiments. However, it will beunderstood by one of ordinary skill in the art that the embodiments maybe practiced without these specific details. For example, softwaremodules, functions, circuits, etc., may be shown in block diagrams inorder not to obscure the embodiments in unnecessary detail. In otherinstances, well-known modules, structures and techniques may not beshown in detail in order not to obscure the embodiments.

Also, it is noted that the embodiments may be described as a processthat is depicted as a flowchart, a flow diagram, a structure diagram, ora block diagram. Although a flowchart may describe the operations as asequential process, many of the operations can be performed in parallelor concurrently. In addition, the order of the operations may berearranged. A process is terminated when its operations are completed. Aprocess may correspond to a method, a function, a procedure, asubroutine, a subprogram, etc., in a computer program. When a processcorresponds to a function, its termination corresponds to a return ofthe function to the calling function or a main function.

Aspects of the systems and methods described below may be operable onany type of general purpose computer system, computing device, or otherprogrammable device, including, but not limited to, a desktop, laptop,notebook, tablet or mobile device. The term “mobile device” includes,but is not limited to, a wireless device, a mobile phone, a smart phone,a wearable electronic device such as a smart watch or head-mounteddisplay device, a mobile communication device, a user communicationdevice, personal digital assistant, mobile hand-held computer, a laptopcomputer, an electronic book reader and reading devices capable ofreading electronic contents and/or other types of mobile devicestypically carried by individuals and/or having some form ofcommunication capabilities (e.g., wireless, infrared, short-range radio,etc.).

1. Overview

The lower limb loading assessment system and methods relate to measuringand monitoring the mechanical loads experienced by musculoskeletaltissue associated with the lower limb, and this is critical to reducingrisk of injury as well as prescribing appropriate training strategies torecover from injury. The high frequency transient loads that travel upthe limb are referred to in this description as ‘tibial shockwaves’. Thelower limb loading assessment system senses, analyses and monitors thesetibial shockwaves to generate or output various feedback metrics and/ordata in the context of various different applications of the lower limbloading assessment system. In general terms, the lower limb loadingassessment system employs body-worn sensors and a subject-specificbiomechanical model to estimate tissue loading. The system employs amechanobiological framework to provide the user with actionable feedbackmetrics to do any one or more of the following: monitor and adjustrunning technique, analyse training sessions, or assess longer-termtissue health. These actionable feedback metrics have application toreduce risk of injury and/or provide meaningful metrics to modify auser's running technique or training regime.

Various embodiments of the systems and methods of the lower limb loadingassessment system will be described. In a first embodiment, a real-timerunning gait feedback system will be described which provides a runnerwith real-time feedback on how loads were being transferred into theirlower limbs and enables them to make adjustments to their runningtechnique to reduce tissue loads for a given running speed based on thatfeedback. In a second embodiment, a cumulative loading monitoring systemwill be described that is capable of monitoring tibial shockwavesthroughout an exercise or activity session, e.g., a run on a particularroute, to identify regions of the run that corresponded to high loads,which might be due to terrain and grade, muscle fatigue, or changes inrunning technique, and feedback that information to the user. Obtaininga cumulative measurement of load could also indicate to the runner iftheir musculoskeletal tissue is at risk of fatigue damage, or whetherthey have received enough load to maintain tissue health over a longertime frame. In a third embodiment, a shoe-fitting feedback system willbe described.

It will be appreciated that the various embodiments of the lower limbloading assessment system to be described may be employed independentlyor may be combined in various forms. The embodiments of the systememploy similar hardware components and aspects of data processing, someof which will first be described below.

Hardware

Referring to FIGS. 1-3, runners 10 exhibit unique style/technique, andit has been identified that runners will exhibit a unique form of tibialshockwave 11 that travels up their lower extremity 12 as they run. Eachrunner 10 can be considered as having their own ‘shock signature’. Thelower limb loading assessment system is configured to sense and record aperson's shock signature as they run using a motion sensor or sensors14, such as accelerometers attached to persons lower limb segment(s) ofinterest.

Tibial shock is a metric for loading at the knee 13, which can bequantified using accelerometry. Using the knowledge of the subject'smass, Newton's second law (F=ma) can be applied to find the total forcetransmitted through the leg 12.

The most common type of running injury is located in the knee, thereforetibial shock is a good surrogate measure of impact force at the knee,which is relatable to the risk of injury. Shin splints (tibial stressreactions or tibial fatigue fractures) are also common examples ofoveruse running injuries and have been associated with increased tibialshock.

Referring to FIG. 1, in the various embodiments of the lower limbloading assessment system, the subject person 10 is provided with one ormore wearable and portable motion sensors on 14 on each or one of theirlower limbs that sense movement as the user runs. The motion sensor 14may be secured to the subject's lower limb 12 by a releasable strap 15,which may be elastic or non-elastic. In some embodiments, the strap 15may comprise a fastening system to tighten the strap around the limbsuch as, but not limited to, a buckle, hook and loop fastening system orsimilar, although this is not essential in the case of some elasticstraps.

In this embodiment, the motion sensor 14 is positioned or attached tothe medial part of the tibia. Typically, the sensor 14 is positionedbetween the femoral epicondyle and medial malleolus. The sensor 14 istypically attached tightly to the limb so as to measure the movementand/or shockwave associated with the underlying bone, rather than themovement of the skin and soft tissue. In one configuration, the sensor14 is located in the region of the distal ⅓^(rd) of the tibia as thisdoes not impinge on the triceps surae muscle group. Additionally, thisregion is ideal for proving haptic or tactile feedback to the user, inthe context of the real-time running gait feedback system embodiment. Inanother configuration, the sensor is positioned just above the lateralmalleolus of the fibula on the subject's ankle.

In this embodiment, the motion sensor 14 is an inertial measurement unit(IMU) and comprises a housing within which an accelerometer sensor 18 ismounted. The accelerometer is configured to measure acceleration withrespect to at least one sensor-axes, but preferably two ormultiple-axes. In this embodiment, the motion sensor is provided with a3-axis accelerometer 18 that is configured to sense and measureaccelerations along three separate sensor-axes. In this embodiment, thethree sensor-axes are orthogonal to each other as shown by the X, Y,Z-axes 20 in FIG. 3. For example, the Z-axis is configured to measureaccelerations in a direction extending along the subject's tibia, theX-axis is configured to measure accelerations in a fore-aft directiontransverse to the Z-axis, and the Y-axis is configured to measureaccelerations in a side-side direction transverse to the Z-axis.

In this embodiment, the motion sensor additionally comprises a 3-axisgyroscope sensor 19 configured to sense angular velocity, and generaterepresentative angular velocity signals, and a 3-axis magnetometersensor 21 configured to sense the earth's magnetic field and generaterepresentative magnetic field signals. In this embodiment, the sensor 14is configured to operate the 3-axis accelerometer, gyroscope, andmagnetometer sensors 18,19,21 concurrently or simultaneously to senseand generate their respective 3-axis sensor signals. In oneconfiguration, the gyroscope and magnetometer sensors are 3-axis sensorsand are aligned or calibrated to have sensor-axes that are co-alignedwith each other and the 3-axis accelerometer 18. In an alternativeconfiguration, the sensors 18, 19, 21 may sense raw signals alongdifferent sensor-axes, but the sensor signals/data generated may beprocessed and transformed into a common 3-axis co-ordinate orsensor-axes system. It will be appreciated that the motion sensor neednot comprise the gyroscope and/or magnetometer sensors in alternativeembodiments.

In this embodiment, the motion sensor 14 further comprises a userinterface 23, such as an on/off switch, buttons, display or touch-screendisplay, to enable the unit to be operated and/or controlled. A powersupply or source 22, such as a battery, rechargeable or otherwise, isprovided to power the circuitry and electronic components of the motionsensor 14. A wireless data communication module 24 is provided that isconfigured to communicate over a wireless data link 25 with a computingdevice 32, to receive control signals or transmit the sensed sensor datato the computing device 32. The motion sensor 14 also comprises acontroller 26, such as a processor or microcontroller or microprocessorfor controlling the components of the motion sensor, along withassociated memory 27 for storing, temporarily or permanently, senseddata from the 3-axis sensors, for processing and/or transmission to thecomputing device 32. One or more operable feedback devices 28 are alsoprovided to provide tactile, audio and/or visual feedback to the wearer,such as vibration devices, auditory devices and/or display or lights.

In this embodiment, the communications module comprises a wirelesstransmitter/receiver that uses a wireless transmission medium or link25, such as Bluetooth, infrared, RF, WiFi, NFC or the like.Alternatively, a hardwired cable connection to the computing device maybe used for the data transmission in other embodiments.

In this embodiment, the motion sensor 14 may be configured tocommunicate directly 25 with the computing device 32 or indirectly 26via an intermediate communications relay device that is operativelyconnected, wirelessly or hardwired, to the computing device 32. In oneexample, the communications relay device may be the wearer's smartphone, smart watch, or another wearable or mobile computing device. Ineither configuration, sensed data may be transmitted continuously or inbatches. It will be appreciated that the sensor signals may be digitallysampled at the desired sampling frequency or otherwise generate digitalsensor signals.

The computing device 32 may be a general purpose computer, such as adesktop, laptop, notebook, or any other form of portable or non-portablecomputing device, including tablet, PDA, smart phone, smart watch,head-mounted display, wearable computer or similar. The computing device32 typically comprises a processor 34, memory 36, display 38, a userinterface 40, such as a keyboard, mouse, touch-screen or similar, and acommunications module 42 for communicating with the motion sensor 14,either directly 25 or indirectly 26. Alternatively, the computing device32 may be a stand-alone processing system. In other configurations, thecomputing device may be in the form of a remote data processing systemor data processing server. For example, the motion sensor 14 maytransmit the sensed data, directly or indirectly, to a cloud-based dataprocessing system.

Data Processing

Depending on the application of the lower limb loading assessmentsystem, the data processing of the sensed data may be carried out indifferent configurations. In some configurations, the motion sensor 14itself carries out all data processing and generates all the requiredfeedback information or metrics for the user, without any exterior dataprocessing. In other configurations, the motion sensor 14 may perform noor minimal data processing and may send the raw sensed data continuouslyor periodically to the computing device 32 for data processing togenerate the feedback information or metrics. It will be appreciatedthat the data processing may be carried out in real-time for someapplications, and at the end of the activity session or a range ofactivity sessions in other applications.

During an assessment session, the user's tibial shockwave datarepresenting the tibial shockwaves experienced during footstrikes withsurface is derived from the 3-axis acceleration data sensed by the3-axis accelerometer 18 in the motion sensor 14. By way of example withreference to FIG. 4A, a portion of measured raw 3-axis acceleration datafor an activity session is shown. A series of discrete foot-strikes isvisible. FIG. 4B shows a close-up of the foot-strike identified as AA inFIG. 4A. The close-up shows the acceleration readings sensed for thefoot-strike in each of the X, Y and Z-axes previously described. Theindividual foot-strikes can be analysed to determine the ‘heel-strike’BB and ‘toe-off’ CC regions, e.g., times, of each foot-strike. Thetime-location of the individual heel-strike and/or toe-off regions canassist in later determining peak acceleration and cadence.

Shock Signatures

Depending on the embodiment, the individuals shock signature may bedefined in various ways. In some embodiments, the shock signature isdefined by the varying profile of the magnitude of each of the 3-axes ofraw acceleration data over a time period, such as for example either foran individual foots-strike (e.g., between ‘heel-strike’ and ‘toe-off’)or the data between the start of each foot-strike for example In otherembodiments, the resultant acceleration magnitude of the 3-axes of rawacceleration data is calculated, and the shock signature may be definedas the profile of the varying resultant acceleration magnitude over atime period, such as for example an individual footstrike or between thestart of successive footstrikes. All conditions being equal (e.g.,terrain, speed, footwear, fatigue level, etc), the individuals shocksignature should substantially repeat for successive footstrikes.

Peak Shock

The data processing is configured to receive and process the raw 3-axisacceleration data (shown in FIGS. 4A and 4B) and calculate the resultantacceleration magnitude data at each time sample. The resultantacceleration magnitude (magnitude of the resultant acceleration vector)at each time sample is calculated as:

ā=√{square root over (a_(x) ² +a _(y) ² +a _(z) ²)}  (1)

The peak shock for each individual foot-strike may be determined by themagnitude of the resultant acceleration vector at the location of theheelstrike in each footstrike, as this is when the maximum shock occursin the foot-strike.

The peak shock is typically dependent on a number of factors including,but not limited to, the user's shoes, the terrain upon which they arerunning, their running technique or style, and any orthotics they areusing. The individual peak shocks for an activity session may beaveraged to generate an average peak shock for that activity session.

Footstrike Pattern

An individual's footstrike pattern affects accumulative load, andtherefore overuse injuries, through varying footstrike magnitudesresulting from a change in running technique. A runner will change theirtechnique for various reasons including, but not limited to, terrainchanges, using different footwear, when fatigue occurs, and in the earlystages of an injury settling in. By way of example, FIG. 5 shows variousfoot-strike patterns of the same runner due to a change in their runningstyle of technique.

Individual runners generate their own unique tibial shock signature whentheir foot comes in contact with the ground during running The resultantacceleration magnitude data may be analysed with pattern recognitionalgorithms to record and store the user's ‘normal’ footstrike pattern.In one configuration, the footstrike pattern may be defined by theprofile of resultant acceleration magnitude between the heelstrike andtoe-off positions in a foot-strike. The data processing may analysewhether the user's sensed footstrike pattern during an activity sessionor part of an activity session deviates beyond a predetermined thresholdrelative to their ‘normal’ stored footstrike pattern, and outputfeedback data representing the time period or periods during theactivity session in which the deviations occurred. The reason for thechange in footstrike pattern may then be identified by reviewing thefactors associated with those time periods in the activity session.

In other configurations, the individual's footstrike patterns fordifferent conditions, e.g., terrain, speed, footwear, or the like may bestored, and pattern recognition algorithms may analyse the tibialshockwave data sensed from an activity session to identify which periodsof the activity session match previously stored footstrike patterns, tothereby enable the terrain, speed, footwear or other aspects of theactivity session to be determined.

Example of Tibial Shockwave Data Sensed for Different Footwear

By way of example, FIGS. 6A and 6B show the resultant accelerationmagnitude data sensed for a number of trials in which the runner wearsdifferent footwear, and in one case runs barefoot. The individualfootstrikes from each session are shown in overlay in FIG. 6B againsttime. FIG. 6A shows the box-and-whisker plots of the peak shocksrecorded for each activity session. As shown, the type of footwear wornby the runner has an impact on the tibial shockwaves experienced by theuser.

Example of Tibial Shockwave Data Sensed Over Different Terrain

By way of example, FIGS. 7A-7D show the resultant acceleration magnitudedata sensed for a number of trials in which the runner runs on differentterrain.

The terrains include road, grass, hard sand, and soft sand. The graphsillustrate the different running shock signatures (e.g., thesubstantially repeating profile of the resultant acceleration magnitudebetween the start of each footstrike) and shock magnitudes from runningon different terrains. It can be seen that the harder the surface, thegreater the shock magnitude, which contributes to a larger accumulativeload.

When comparing road to sand, the shock signature (e.g., the profile ofthe resultant acceleration magnitude as it varies between successivefootstrikes) also changes dramatically. The softer the surface, thegreater the amount of leg movement (an example of which is circled ineach graph). As noted above, this change in shock signature (e.g.,footstrike pattern) can be used to identify what kind of running therunner is doing (i.e. what surface they are running on and how theirtechnique changes to adjust for a change in surface).

FIG. 7E shows the box-and-whisker plot of the peak shocks recorded foreach terrain, from the data in FIGS. 7A-7D.

FIG. 7F show an overlay of 11 steps/footstrikes for each terrain fromthe data in FIGS. 7A-7D. It can be seen that the shock signature of eachdifferent step is in fact a recurring pattern dependent on the type ofterrain. The thicker dark line represents the average shock signature.

Example Algorithm for Determining Cadence and Session Load Stimulus

By way of example, an algorithm for determining cadence and session loadstimulus from sensed tibial shockwave data recorded for a runner over anactivity session will be described with reference to FIG. 8. In thisexample, the algorithm 50 executes once the full activity session datais available, i.e. post-processing, but it will be appreciated that thealgorithm may be begin executed concurrently with the generation of thesensed tibial shockwave data once enough representative data isavailable to generate reliable results in alternative configurations. Asdescribed earlier, the data processing performed by the algorithm may beexecuted onboard the motion sensor 14 itself, or the algorithm may beoperating on a remote computing device 32 communicatively coupled orconnected (e.g., wireless or hardwired) to the motion sensor 14 whichreceives and processes the sensed data from the motion sensor.

The algorithm 50 starts by receiving the tibial shockwave data from themotion sensor 14 attached to the runner's lower limb at step 52. In thisexample, the tibial shockwave data is in the form of raw 3-axisacceleration data sensed by the 3-axis accelerometer of the motionsensor 14. The raw 3-axis acceleration data is typically provided indigital form as a time-series, sampled from the analogue accelerationsignals. However, in alternative configurations the analogue signal maybe received and digitised by the algorithm. An acceleration magnitudevector is then calculated for the received 3-axis acceleration data togenerate resultant acceleration magnitude data at step 54. Thisresultant acceleration magnitude data is calculated using equation (1)above, i.e. by square rooting the sum of the squares of all 3acceleration measures at each time-sample.

The resultant acceleration magnitude data is then filtered to removenoise at step 56. In this example, the data is subjected to a bandpassfilter that is configured to filter out excessively high frequency noisesuch as skin movement, and also very low frequency movement that is muchbelow the frequency of a runner. The filtered resultant accelerationmagnitude data is then processed to identify the fundamental frequencyat step 58. In this example, the fundamental frequency is identifiedusing a fast fourier transform and wavelet techniques. The value andpower of the identified fundamental frequency is then reviewed againstthreshold ranges to determine whether it is within an appropriate rangefor running data.

As shown at step 60, the filtered acceleration magnitude data is thenfiltered further, this time at double the fundamental frequency (thenyquist frequency) to determine the approximate time location of theheelstrikes in the data. A running window is then applied in step 60 tothe data to find the exact time location of the heelstrikes by searchingnear the previously determined approximate location of the individualheelstrikes.

Cadence associated with the activity session is then determined byanalysing the determined time locations of the individual heelstrikes atstep 62. In this example, data indicative of the cadence is generated bycalculating the average time between each identified heelstrike.

The peak shock associated with each discrete footstrike in the data,i.e. the magnitude of the resultant acceleration magnitude data at theidentified heelstrike locations, is then extracted. This peak shock datais then input into an algorithm that calculates a session load stimulus(eDLS), an example of which is described below in relation to the secondembodiment and equation (2).

2. First Embodiment—Real-time running gait feedback system The shape andform of an individual's tibial ‘shock signature’ allows thequantification of metrics such as runner deviation, i.e. the deviationfrom their normal signature shock. This means that changes in therunner's gait can be identified, and with the right processing tools wecan quantify these differences and infer smart conjectures about how therunner should alter their gait to return to their best form. Othermetrics such as cadence can also be measured.

The use of tri-axial accelerometry to measure individual and resultanttibial shock allows the analysis of a subject's foot strike in threedimensions, which can be used to assist in changing the subject'srunning technique if it means they will receive less tibial shock. Thesensors ability to reproduce body movement in 3D space can be used tovisualize a person's technique in a virtual simulation program (e.g.,OpenSim, Stanford Calif.).

Referring to FIGS. 9-10C, an implementation of the lower limb loadingassessment system as a real-time running gait feedback system will bedescribed. Referring firstly to the feedback loop in FIG. 9, thefeedback system is intended to provide a runner with real-time feedbackabout their running style, and whether it has deteriorated, during anactivity session, such as running a route. In particular, the runningmay be running a route as shown at 70. During that run, the runner'sform, technique or style may change, e.g., due to fatigue, injury,change in speed, terrain or some other reason, as shown at 72. Thechange in the runner's form results in a changed shock signature sensedby the motion sensor 14 attached to their lower limb as indicated at 74.This change in shock signature is detected by the feedback system, andif significant triggers the initiation of a feedback alert to the user.

The feedback system may be configured to trigger a feedback alert basedon one or more selected changes in the shock signature relative to theuser's normal signature. In one configuration, the peak shock associatedwith each footstrike is compared with a threshold, and an alert isgenerated if the peak shock exceeds the threshold. In anotherconfiguration, a moving average peak shock is calculated andcontinuously or periodically compared to a threshold, and an alert isgenerated if the average peak shock exceeds the threshold. In anotherconfiguration, the footstrike pattern associated with each footstrike iscompared with the user's stored ‘normal’ shock signature, either inthree dimensions with respect to each acceleration axis or on the basisof the profile of the resultant acceleration magnitude data of eachfootstrike, and an alert is generated if the footstrike pattern deviatesbeyond a predetermined range relative to the normal shock signature. Itwill be appreciated that one or more of the previous comparisons may becarried out in data processing concurrently to decide whether togenerate an alert. Additonally, a range of different types of alerts maybe provided depending on the change in the running style, or themagnitude of the alert may vary according to the magnitude of thedeviation from the user's normal running style.

If an alert is triggered, an alert control signal is generated, and thiscauses tactile, audio and/or visual feedback to be provided to therunner to alert them to the deterioration in the running style at 76. Inresponse to the feedback, the runner adjusts their form at 78 until thealert ceases to thereby return their running style to the desired formfor injury mitigation.

The hardware configuration of the real-time running gait feedback systemmay be provided in various configurations, some embodiments of whichwill be described with reference to FIGS. 10A-10C. The configurationscorrespond or are based around the system previously described withreference to FIGS. 1-3. The example configurations will be describedwith reference the main components of the system, namely the motionsensor comprising the 3-axis accelerometer for sensing the tibialshockwave data, the data processing, and feedback device(s). Theconfigurations show that these components may be combined in a singledevice or alternatively dispersed amongst two or more separate butcommunicatively coupled devices.

Referring to a first configuration 80 in FIG. 10A, the feedback systemmay be embodied in a single device, namely the motion sensor 82 worn bythe user. In particular, the motion sensor 82 comprises theaccelerometer sensor 84, data processor 86, and the feedback device ordevices 88. For example, the data processor generates an alert controlsignal when processing the sensor data when detecting a deviation in theuser's tibial shockwave data, and the alert control signal is configuredto operate one or more feedback devices onboard the motion sensor. Thefeedback device(s) may comprise a tactile vibration device or element,and/or an auditory component for generating an audible alert. As themotion sensor 82 is mounted to the user's lower limb, they will feel thevibration at their lower limb or the audible alert emanating from thesensor on their lower limb.

Referring to a second configuration 90 in FIG. 10B, the feedback systemis implemented by a motion sensor 92 worn on the user's lower limb aspreviously described and which comprises at least the 3-axisaccelerometer 93, and which is communicatively coupled, e.g., over awireless data connection, to a portable or wearable computing device 94held, worn or otherwise attached or carried by the user. By way ofexample, the computing device 94 may be a smart phone or smart watch,and the computing device comprises the data processor 96 and feedbackdevice(s) 98. In particular, the raw acceleration data is transmittedfrom the motion sensor onboard the user's lower limb to their smartphone or smart watch which they are carrying, holding or otherwisewearing. The received tibial shockwave data is processed, and therelevant alert control signals are triggered when the runner's styledeviates as previously described. By way of example, the data processor96 is implemented by the processor of the smart phone or smart watch,and the feedback device(s) 98 may comprise the vibration or audio outputcomponents or hardware of the smart phone or smart watch.

Referring to a third configuration 100 in FIG. 10C, the motion sensor,data processor and feedback device(s) may be separate components worn orcarried by the user that are all communicatively coupled over one ormore data links, whether wireless or hardwired. For example, the motionsensor 102 with accelerometer 104 is worn on the user's lower limb andtransmits the sensed tibial shockwave data to the data processor 108onboard a portable or wearable computing device 106, e.g., a smart phoneor smart watch. The computing device 106 then operates or controls oneor more feedback devices worn or carried by the user via alert controlsignals. The feedback devices may comprise any one or more of auditoryfeedback devices 110, such as buzzers or similar, tactile feedbackdevices 112, such as vibrator devices or similar, and/or visual feedbackdevices 114, such as LED lights or display devices.

3. Second Embodiment—A Cumulative Load Monitoring System

Referring to FIG. 11, a cumulative load monitoring system embodiment ofthe lower limb loading assessment system will be described. The systememploys the general hardware system and components discussed withreference to FIGS. 1-3. The cumulative load monitoring system isconfigured to generate a Daily Load Stimulus (DLS) metric in response tothe tibial shockwave data sensed by the motion sensor when the user isengaged in activity sessions throughout the day.

Musculoskeletal tissue, such as bone, adapts to its mechanicalenvironment by sensing the local tissue deformations (strains). DailyLoad Stimulus (DLS) uses tissue stress as a key indicator for load. TheDLS is important because it is a method that quantifies the daily stresshistories of bone in terms of daily cyclic stress magnitudes and thenumber of daily loading cycles (i.e. total loading exposure). Thisinformation aids in defining the amount of stresses and loads imposed onthe bones within the leg over longer time periods (e.g., days) ratherthan transient ones (e.g., one foot strike).

The sensed data from the motion sensor 14 can also be used to quantifyand monitor the DLS of an individual when they are engaged in activitysessions, e.g., running In particular, in this cumulative loadmonitoring system the subject can be provided with a motion sensor 14that they wear when engaged in physical activity sessions and which isconfigured to continuously or periodically transmit sensed tibial shockdata to the computing device 32 or a server for processing andmonitoring. As previously described, the motion sensor 14 mayperiodically or continuously transmit the sensed data to any computingdevice 32 in communication range, such as a smart phone or smart watchcarried or worn by the user over Bluetooth or any other wirelesscommunication medium, or alternatively may be provided with acommunication module that can communicate over a cellular connection,WiFi, or any other direct wireless data communication medium to theremote computing device 32 or server. Alternatively, the motion sensor14 may store the data in onboard memory 27 for later download to acomputing device when in range or otherwise operatively connected, e.g.,by cable.

The cumulative load monitoring system is able to quantify anindividual's daily load stimulus (DLS). The accumulative load monitoringsystem in this embodiment accounts for variables such as saturation andrecovery of osteogenic potential with cyclical loading and standing.

Referring to FIG. 11, the process 100 of the accumulative loadmonitoring system is shown. The final equation:

$\begin{matrix}{{eDLS} = \left\lbrack {\Sigma_{j = 1}^{k}{n_{j}\left( {Gz}_{j} \right)}^{m}} \right\rbrack_{perday}^{\frac{1}{2m}}} & (2)\end{matrix}$

is the estimated daily load stimulus (DLS) for the individual, where:

-   -   Gz=the peak magnitude of the force derived from F=m.a    -   j=number of loading conditions    -   m=weighting factor (e.g., 4)    -   k=number of different loading conditions

Firstly, sensor data 120 is transmitted wirelessly from a motion sensor14 worn by the user, as described previously, to a reciprocatingreceiver, such as a computing device 32. The receiver may be, but is notlimited to, a smart phone, smart watch, or any other portable computingdevice 32 having a communication module such as Bluetooth 4.0 orsimilar. The sensor data 120 comprises linear accelerations in 3 axes,but may also include angular rates in 3 axes and magnetic field strengthin 3 axes, if gyroscope 19 and/or magnetometer 20 sensors are alsoprovided in the motion sensor 14 in order to get more information aroundsensor orientation.

Once the sensor data 120 is received at the computing device, the datais processed by algorithms that calculate the daily load stimulus (DLS)for the individual. The process that the algorithm runs through is asfollows:

-   -   a. The magnitude of the acceleration vector is calculated by:        a=√{square root over (a_(x) ²+a_(y) ²+a_(z) ²)}    -   b. The magnitude time series data (resultant acceleration        magnitude data) is then run through an algorithm that detects        and quantifies the peaks present, i.e. peak shock data (similar        to that detected in the example algorithm 50 described        previously). The peaks are directly related to the impact phase        of a running stride. Each runner has a stored tibial ‘shock        signature’ that is used in the process to detect future tibial        shock impacts. The algorithm does the detection by using        cross-correlation. Using cross-correlation, and other time        series analysis techniques, such as Fourier Transforms, and        Power Spectral Densities (PSD), the algorithm is able to        quantify whether the athlete is running, walking, or resting,        and this is shown generally at step 122.    -   c. Once all of these activities have been quantified and        individual tibial shock peaks identified 124, the peak        accelerations are recorded and stored 126 for each impact phase        in both running and walking.    -   d. The stored data is continually updated and processed        calculating the cumulative load stimulus 128, taking into        account the different effects of running, walking, standing, and        recovery.    -   e. Bone Stimulus Saturation is taken into account by:        -   i. Once saturation has been reached the peak tibial            accelerations are multiplied by the hyperbolic function            1/(1+N) where N is the number of cyclic loads after            saturation. This models the cumulative load after saturation            is reached. Saturation was assumed after 5 min of continuous            running, 10 min of continuous walking or equivalent. This            threshold however is context dependent and may vary based on            factors such as age and sex.        -   ii. Recovery is then modeled by the equation            100(1−e^(−t/τ)), where t is time in hours between bouts and            r is a time constant (2 hours). Each successive bout of            walking or running that occurred after saturation was then            multiplied by the recovery equation.        -   iii. Once the data has been segmented into their respective            activities (i.e. running, walking, rest etc.), the            magnitudes of the tibial accelerations are stored in a            buffer that is continually processed by eDLS equation.        -   iv. Using the eDLS equation we can quantify Bone Stimulus            Saturation and Tibial Shock over longer time periods rather            than smaller time transients.

The calculated estimate daily load stimulus can be utilised in variousapplications, some examples of which are set out below.

Footwear Application

The monitored eDLS generated by the cumulative load monitoring systemmay be compared to a threshold level for the individual for the purposeof identifying when the individual's footwear may be deteriorating or nolonger providing adequate attenuation of the tibial shockwaves. If theeDLS exceeds the threshold level, the individual may be alerted ornotified by the system that their running shoes no longer reduce tibialshock to adequate levels.

Activity Session Load Stimulus Application

The above algorithm for estimating daily load stimulus is explained inthe context of combining tibial shockwave data over a plurality ormultiple activity sessions in a day. However, it will be appreciatedthat the algorithm may also be applied to a single set of tibialshockwave data from a single activity session and in this context thecalculated eDLS represents a session load stimulus (SLS).

4. Third Embodiment—Shoe-Fitting Feedback System

With reference to FIGS. 12A-15, a shoe-fitting feedback systemembodiment of the lower limb loading assessment system will bedescribed. The shoe-fitting system employs the same hardwareconfiguration and components as described with reference to FIGS. 1-3and is configured to quantify the different levels of tibial shock thatarise from running with different respective pairs of shoes. Thisinformation is used to provide a customer with a pair of shoes thatsuits their own personal gait. The shoe-fitting system uses a tibialshock metric, such as a tibial shock score, to identify a pair of shoesthat reduce loading to the knee of a subject person. Optionally, theshoe-fitting system may also comprise a treadmill or similar exerciseplatform, provided with forward and backward facing cameras capturingthe subject's running style on the treadmill.

Different shoes exhibit varying levels of stiffness and cushioning,leading to differences in the attenuation of shock into the lower limbduring running Therefore, the type of shoe that a person wears when theyrun will affect the magnitude as well as distribution of the travellingshockwave, which in turn will change the force profile transmitted tothe leg and hence chance of injury for that individual.

The typical shoe-fitting process using the shoe-fitting feedback systemwill now be described in further detail, by way of example only. Acustomer comes into a store, has their feet measured, and tries on apair of shoes suggested by the shop assistant. The motion sensor 14 isthen strapped on to one of the customer's lower legs, for example at aposition just above the lateral malleolus of the fibula on the subject'sankle. The motion sensor 14 is then switched on using the user interface23 provided on the motion sensor 14. The customer is then asked to geton the treadmill, and the shop assistant speeds the treadmill up to aconstant speed ensuring the customer is running at comfortable pace.

During this activity session, the motion sensor 14 is configured tomeasure each discrete tibial shockwave experienced by the customer'smonitored lower leg as they run and generates representative tibialshockwave data. Referring to FIG. 4B, an example of the accelerationsmeasured in the three sensor axes 20 for a single foot-strike on thetreadmill is shown. The tibial shockwave data transmitted to thecomputing device 32 represents a series of discrete tibial shockwaves,like the data shown in FIG. 4A, for each foot-strike as the customerruns for the activity session, which may be monitored for apredetermined time period, say 20-30 seconds for example, although thismay be longer or shorter depending on the circumstances.

In this embodiment, the computing device 32 is configured to receive theraw three-axis acceleration data and is configured to calculate atime-series of the resultant acceleration magnitude data representingthe tibial shockwave data from the motion sensor. Alternatively, theresultant acceleration magnitude data may be calculated onboard themotion sensor and transmitted to the receiver 28 for the computingdevice 32.

At the end of each activity session, the computing device 32 isconfigured to determine the peak resultant acceleration magnitude foreach discrete tibial shockwave in the series of foot strikes from theactivity session. The computing device is then configured to calculatethe average peak resultant acceleration magnitude of the foot strikesfor the activity session, and this is output or stored directly as atibial shock score for the activity session, or alternatively theaverage peak resultant acceleration magnitude is converted into anormalized value within a predetermined tibial shock score scale andoutput or stored as the tibial shock score for the activity session. Theaverage peak resultant acceleration magnitude may also be normalisedwith respect to the person's body weight, speed, or effective body mass(taking into account the degree of knee flexion).

In one embodiment, the computing device 32 may receive user inputidentification data identifying the type of footwear being worn by thecustomer during the activity session. The computing device 32 is thenable to link the tibial shockwave data and/or tibial shock score fromthe activity session to the particular footwear being worn.

The above process is then repeated for the customer for a plurality ofdifferent types of shoes and the tibial shockwave data for each activitysession or shoe trialled is sensed, processed and stored as above.

Once all shoes have been trialled through a respective activity sessionon the treadmill, the computing device 32 is configured to undertake acomparative analysis of the data from each activity session and generateassessment data to assist the shop assistant in recommending orselecting the footwear that is likely to result in reduced lower limbimpact for the customer.

By way of example, FIGS. 12A-12C show graphs of results of the resultantacceleration magnitude data sensed over 4 separate activity sessions,for three different runners. In each separate activity session, therunner wore a different type of running shoe (shoes 1-4). In the graphs,the discrete tibial shockwaves for each foot strike in the monitoredperiod are overlaid upon each other rather than serially presented inthe timeline. FIGS. 13A-13C depict the corresponding box-and-whiskerplots of the data depicted in FIGS. 12A-12C respectively.

The computing device 32 may generate and display assessment data basedon the collective data from the activity sessions. The computing device32 may be configured to display graphs as above in FIGS. 12A-12C, orcorresponding data tables for example The computing device 32 may alsobe configured to generate assessment data based on comparison of thedata from the activity sessions. In one form, the computing device 32may output or display data indicative of the activity session and/orshoe having the lowest associated tibial shock score and therefore isthe optimal shoe for the customer and their running style.Alternatively, the computing device 32 may output or display the tibialshock score associated with each shoe trialled for the shop assistantand customer to review and consider.

Reverting to FIGS. 13A-13C, it can be seen that the 3 different runnerseach had unique tibial shockwave data when trialling the 4 differentshoes, and the same type of shoe is not necessarily optimal for eachrunner.

In an embodiment, the customer's tibial shockwave data for each shoe maybe stored in the computing device 32 or an associated database orstorage medium for future use. For example, referring to the flow chartof FIG. 14, the customer may be sent a reminder to return to the shoestore after 6-12 months of using their shoes, to re-assess their tibialshock score when the shoes are in a worn-state. Depending on the resultsrelative to the original tibial shock score in a new state, a new pairof shoes may be recommended, or the shoes may be deemed to still be inadequate condition for further use. In another example, referring toFIG. 15, the customer may be provided with a motion sensor for wearingwhile running over a time period, say 6-12 months. The motion sensorsends the tibial shockwave data to the computing device during eachrunning session and is configured to assess the tibial shockwave data todetermine when the customer's running shoes no longer reduce tibialshock to an adequate level. This analysis may be based on comparing thetibial shockwave data or parameters extracted from the tibial shockwavedata, such as peak shock, average peak shock, and/or shock signature, topredetermined thresholds or threshold ranges.

5. General

Furthermore, embodiments may be implemented by hardware, software,firmware, middleware, microcode, or any combination thereof. Whenimplemented in software, firmware, middleware or microcode, the programcode or code segments to perform the necessary tasks may be stored in amachine-readable medium such as a storage medium or other storage(s). Aprocessor may perform the necessary tasks. A code segment may representa procedure, a function, a subprogram, a program, a routine, asubroutine, a module, a software package, a class, or any combination ofinstructions, data structures, or program statements. A code segment maybe coupled to another code segment or a hardware circuit by passingand/or receiving information, data, arguments, parameters, or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, etc.

In the foregoing, a storage medium may represent one or more devices forstoring data, including read-only memory (ROM), random access memory(RAM), magnetic disk storage mediums, optical storage mediums, flashmemory devices and/or other machine readable mediums for storinginformation. The terms “machine readable medium” and “computer readablemedium” include, but are not limited to, portable or fixed storagedevices, optical storage devices, and/or various other mediums capableof storing, containing or carrying instruction(s) and/or data.

The various illustrative logical blocks, modules, circuits, elements,and/or components described in connection with the examples disclosedherein may be implemented or performed with a general purpose processor,a digital signal processor (DSP), an application specific integratedcircuit (ASIC), a field programmable gate array (FPGA) or otherprogrammable logic component, discrete gate or transistor logic,discrete hardware components, or any combination thereof designed toperform the functions described herein. A general purpose processor maybe a microprocessor, but in the alternative, the processor may be anyconventional processor, controller, microcontroller, circuit, and/orstate machine. A processor may also be implemented as a combination ofcomputing components, e.g., a combination of a DSP and a microprocessor,a number of microprocessors, one or more microprocessors in conjunctionwith a DSP core, or any other such configuration.

The methods or algorithms described in connection with the examplesdisclosed herein may be embodied directly in hardware, in a softwaremodule executable by a processor, or in a combination of both, in theform of processing unit, programming instructions, or other directions,and may be contained in a single device or distributed across multipledevices. A software module may reside in RAM memory, flash memory, ROMmemory, EPROM memory, EEPROM memory, registers, hard disk, a removabledisk, a CD-ROM, or any other form of storage medium known in the art. Astorage medium may be coupled to the processor such that the processorcan read information from, and write information to, the storage medium.In the alternative, the storage medium may be integral to the processor.

One or more of the components and functions illustrated the figures maybe rearranged and/or combined into a single component or embodied inseveral components without departing from the invention. Additionalelements or components may also be added without departing from theinvention. Additionally, the features described herein may beimplemented in software, hardware, as a business method, and/orcombination thereof.

In its various aspects, the invention can be embodied in acomputer-implemented process, a machine (such as an electronic device,or a general purpose computer or other device that provides a platformon which computer programs can be executed), processes performed bythese machines, or an article of manufacture.

Such articles can include a computer program product or digitalinformation product in which a computer readable storage mediumcontaining computer program instructions or computer readable datastored thereon, and processes and machines that create and use thesearticles of manufacture.

The foregoing description of the invention includes preferred formsthereof. Modifications may be made thereto without departing from thescope of the invention as defined by the accompanying claims.

What is claimed is:
 1. A cumulative load monitoring system for lowerlimbs of a subject, the system comprising: one or more motion sensorssecured or mounted to one of or both of the subject's lower limbs, thesensor(s) being configured to sense the tibial shockwaves experienced bythe lower limb(s) as the subject engages in physical activity involvingrepetitive footstrikes of the lower limb(s) with a surface and generaterepresentative tibial shockwave data; and a data processor that isconfigured to receive the tibial shockwave data sensed by the one ormore sensors, and wherein the processor is configured to process thereceived tibial shockwave data and generate feedback data in the form ofan estimate of the subject's cumulative load stimulus over a time periodor one or more activity sessions.
 2. The cumulative load monitoringsystem according to claim 1 wherein the data processor is configured tocompare the cumulative load stimulus to a threshold stored for thesubject and generate an alert or notification if the threshold isexceeded.
 3. The cumulative load monitoring system according to claim 1wherein the data processor is configured to generate the feedback dataof an estimate of cumulative load stimulus based at least partly onidentifying and extracting peak shock data from the received tibialshockwave data.
 4. The cumulative load monitoring system according toclaim 1 wherein the or each motion sensor comprises an accelerometerthat is configured to sense acceleration data in at least three axes andgenerate representative acceleration data as the subject engages inphysical activity, and the representative tibial shockwave datagenerated by the or each motion sensor comprises the acceleration datawhich represents a series of discrete tibial shockwaves from thediscrete footstrikes.
 5. The cumulative load monitoring system accordingto claim 4 wherein the data processor is configured to generate thefeedback data of an estimate of cumulative load stimulus based at leastpartly on identifying and extracting peak shock data from the receivedtibial shockwave data.
 6. The cumulative load monitoring systemaccording to claim 5 wherein the data processor is configured toidentify and extract the peak shock data from the received tibialshockwave data by converting the 3-axes acceleration data of the tibialshockwave data into resultant acceleration magnitude data and extractingpeak shock data representing the peak resultant acceleration magnitudedata associated with each discrete footstrike.
 7. The cumulative loadmonitoring system according to claim 1 wherein the data processor isconfigured to receive tibial shockwave data from a single activitysession engaged in by the subject and generates the feedback data of anestimate of the subject's cumulative load stimulus in the form of asession load stimulus.
 8. The cumulative load monitoring systemaccording to claim 1 wherein the data processor is configured to receivetibial shockwave data from multiple activity sessions engaged in by thesubject and generate feedback data of an estimate of the subject'scumulative load stimulus relating to those multiple activity sessions.9. The cumulative load monitoring system according to claim 1 whereinthe data processor is configured to receive tibial shockwave data from aplurality of activity sessions engaged in by the subject from a singleday and generate the feedback data of an estimate of the subject'scumulative load stimulus in the form of a daily load stimulus (DLS). 10.The cumulative load monitoring system according to claim 9 wherein thedata processor is configured to compare the generated DLS to a thresholdDLS stored for the subject and generate an alert or notification if thethreshold is exceeded.
 11. The cumulative load monitoring systemaccording to claim 9 wherein the data processor is configured togenerate the DLS by: processing the received tibial shockwave data tosegment the data into predefined types of activities; extracting peakshock data from each segment of data; and determining the DLS based onthe peak shock data and its associated type of activity.
 12. Thecumulative load monitoring system according to claim 11 wherein thepredefined types of activities comprise: running, walking, and resting.13. The cumulative load monitoring system according to claim 9 whereinthe data processor is further configured to apply a bone stimulussaturation model for saturation and recovery when generating the DLS.14. The cumulative load monitoring system according to claim 1 whereinthe data processor is configured to continuously update the feedbackdata representing an estimate of cumulative load stimulus based on newreceived tibial shockwave data relating to the time period or one ormore activity sessions.
 15. The cumulative load monitoring systemaccording to claim 1 wherein the data processor is communicativelycoupled to the one or more motion sensors over a data link.
 16. Thecumulative load monitoring system according to claim 1 wherein the dataprocessor is onboard the or each motion sensor.
 17. The cumulative loadmonitoring system according to claim 1 wherein the one or more motionsensors are secured or mounted to the subject's lower limb in any of thefollowing locations: between the femoral epicondyle and medialmalleolus, in the region of the lower ⅓^(rd) of the tibia, in the regionof the medial part of the tibia, in the region adjacent and above themedial malleolus of the tibia, or in the region adjacent and above thelateral malleolus of the tibia.
 18. A method of assessing lower limbshock of a subject, comprising: receiving tibial shockwave data from oneor more motion sensors secured or mounted to one of or both of thesubject's lower limbs, the sensor(s) being configured to sense thetibial shockwaves experienced by the lower limb(s) as the subjectengages in physical activity involving repetitive footstrikes of thelower limb(s) with a surface and generates representative tibialshockwave data; processing the received tibial shockwave data; andgenerating feedback data in the form of an estimate of the subject'scumulative load stimulus over a time period or one or more activitysessions.
 19. The method according to claim 18 further comprisingcomparing the cumulative load stimulus to a threshold stored for thesubject and generating an alert or notification if the threshold isexceeded.
 20. The method according to claim 18 wherein generatingfeedback data in the form of an estimate of the subject's cumulativeload stimulus is based at least partly on identifying and extractingpeak shock data from the received tibial shockwave data.